
import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def    SCREAMING_SNAKE_CASE__		( lowerCAmelCase_				:			Any       ,lowerCAmelCase_				:			Optional[int]=1							)    ->   List[Any]:







							"""simple docstring"""






							if n_shave_prefix_segments >= 0:
														return ".".join(path.split('.'							)[n_shave_prefix_segments:]							)
							else:
														return ".".join(path.split('.'							)[:n_shave_prefix_segments]							)
def    SCREAMING_SNAKE_CASE__		( lowerCAmelCase_				:			str       ,lowerCAmelCase_				:			int=0							)    ->   Any:







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_	:							Any											=[]
							for old_item in old_list:
														SCREAMING_SNAKE_CASE_	:							Union[str, Any]											=old_item.replace('in_layers.0'       ,'norm1'							)
														SCREAMING_SNAKE_CASE_	:							Optional[int]											=new_item.replace('in_layers.2'       ,'conv1'							)

														SCREAMING_SNAKE_CASE_	:							List[str]											=new_item.replace('out_layers.0'       ,'norm2'							)
														SCREAMING_SNAKE_CASE_	:							List[str]											=new_item.replace('out_layers.3'       ,'conv2'							)

														SCREAMING_SNAKE_CASE_	:							Optional[Any]											=new_item.replace('emb_layers.1'       ,'time_emb_proj'							)
														SCREAMING_SNAKE_CASE_	:							List[str]											=new_item.replace('skip_connection'       ,'conv_shortcut'							)

														SCREAMING_SNAKE_CASE_	:							List[str]											=shave_segments(lowerCAmelCase_       ,n_shave_prefix_segments=lowerCAmelCase_							)

														mapping.append({'old': old_item, 'new': new_item}							)

							return mapping
def    SCREAMING_SNAKE_CASE__		( lowerCAmelCase_				:			Union[str, Any]       ,lowerCAmelCase_				:			int=0							)    ->   Dict:







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_	:							List[str]											=[]
							for old_item in old_list:
														SCREAMING_SNAKE_CASE_	:							Union[str, Any]											=old_item

														SCREAMING_SNAKE_CASE_	:							Optional[Any]											=new_item.replace('norm.weight'       ,'group_norm.weight'							)
														SCREAMING_SNAKE_CASE_	:							List[str]											=new_item.replace('norm.bias'       ,'group_norm.bias'							)

														SCREAMING_SNAKE_CASE_	:							Any											=new_item.replace('proj_out.weight'       ,'proj_attn.weight'							)
														SCREAMING_SNAKE_CASE_	:							Any											=new_item.replace('proj_out.bias'       ,'proj_attn.bias'							)

														SCREAMING_SNAKE_CASE_	:							Dict											=shave_segments(lowerCAmelCase_       ,n_shave_prefix_segments=lowerCAmelCase_							)

														mapping.append({'old': old_item, 'new': new_item}							)

							return mapping
def    SCREAMING_SNAKE_CASE__		( lowerCAmelCase_				:			List[Any]       ,lowerCAmelCase_				:			Union[str, Any]       ,lowerCAmelCase_				:			str       ,lowerCAmelCase_				:			Any=None       ,lowerCAmelCase_				:			List[Any]=None       ,lowerCAmelCase_				:			Dict=None							)    ->   Any:







							"""simple docstring"""






							assert isinstance(lowerCAmelCase_       ,lowerCAmelCase_							), "Paths should be a list of dicts containing 'old' and 'new' keys."

							# Splits the attention layers into three variables.
							if attention_paths_to_split is not None:
														for path, path_map in attention_paths_to_split.items():
																					SCREAMING_SNAKE_CASE_	:							List[Any]											=old_checkpoint[path]
																					SCREAMING_SNAKE_CASE_	:							Optional[int]											=old_tensor.shape[0] // 3

																					SCREAMING_SNAKE_CASE_	:							Any											=(-1, channels) if len(old_tensor.shape							) == 3 else (-1)

																					SCREAMING_SNAKE_CASE_	:							Dict											=old_tensor.shape[0] // config["""num_head_channels"""] // 3

																					SCREAMING_SNAKE_CASE_	:							List[str]											=old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]							)
																					SCREAMING_SNAKE_CASE_	:							Optional[int]											=old_tensor.split(channels // num_heads       ,dim=1							)

																					SCREAMING_SNAKE_CASE_	:							List[str]											=query.reshape(lowerCAmelCase_							)
																					SCREAMING_SNAKE_CASE_	:							str											=key.reshape(lowerCAmelCase_							)
																					SCREAMING_SNAKE_CASE_	:							Optional[int]											=value.reshape(lowerCAmelCase_							)

							for path in paths:
														SCREAMING_SNAKE_CASE_	:							List[str]											=path["""new"""]

														# These have already been assigned
														if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																					continue

														# Global renaming happens here
														SCREAMING_SNAKE_CASE_	:							Optional[Any]											=new_path.replace('middle_block.0'       ,'mid_block.resnets.0'							)
														SCREAMING_SNAKE_CASE_	:							List[str]											=new_path.replace('middle_block.1'       ,'mid_block.attentions.0'							)
														SCREAMING_SNAKE_CASE_	:							Tuple											=new_path.replace('middle_block.2'       ,'mid_block.resnets.1'							)

														if additional_replacements is not None:
																					for replacement in additional_replacements:
																												SCREAMING_SNAKE_CASE_	:							Union[str, Any]											=new_path.replace(replacement['old']       ,replacement['new']							)

        # proj_attn.weight has to be converted from conv 1D to linear
														if "proj_attn.weight" in new_path:
																					SCREAMING_SNAKE_CASE_	:							Tuple											=old_checkpoint[path["""old"""]][:, :, 0]
														else:
																					SCREAMING_SNAKE_CASE_	:							Union[str, Any]											=old_checkpoint[path["""old"""]]
def    SCREAMING_SNAKE_CASE__		( lowerCAmelCase_				:			int       ,lowerCAmelCase_				:			Optional[int]							)    ->   int:







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_	:							Union[str, Any]											={}

							SCREAMING_SNAKE_CASE_	:							int											=checkpoint["""time_embed.0.weight"""]
							SCREAMING_SNAKE_CASE_	:							List[Any]											=checkpoint["""time_embed.0.bias"""]
							SCREAMING_SNAKE_CASE_	:							List[Any]											=checkpoint["""time_embed.2.weight"""]
							SCREAMING_SNAKE_CASE_	:							str											=checkpoint["""time_embed.2.bias"""]

							SCREAMING_SNAKE_CASE_	:							Union[str, Any]											=checkpoint["""input_blocks.0.0.weight"""]
							SCREAMING_SNAKE_CASE_	:							Optional[int]											=checkpoint["""input_blocks.0.0.bias"""]

							SCREAMING_SNAKE_CASE_	:							List[str]											=checkpoint["""out.0.weight"""]
							SCREAMING_SNAKE_CASE_	:							Tuple											=checkpoint["""out.0.bias"""]
							SCREAMING_SNAKE_CASE_	:							Tuple											=checkpoint["""out.2.weight"""]
							SCREAMING_SNAKE_CASE_	:							Optional[Any]											=checkpoint["""out.2.bias"""]

							# Retrieves the keys for the input blocks only
							SCREAMING_SNAKE_CASE_	:							Optional[int]											=len({'.'.join(layer.split('.'							)[:2]							) for layer in checkpoint if 'input_blocks' in layer}							)
							SCREAMING_SNAKE_CASE_	:							Optional[Any]											={
							    layer_id: [key for key in checkpoint if F"""input_blocks.{layer_id}""" in key]
							    for layer_id in range(lowerCAmelCase_							)
							}

							# Retrieves the keys for the middle blocks only
							SCREAMING_SNAKE_CASE_	:							str											=len({'.'.join(layer.split('.'							)[:2]							) for layer in checkpoint if 'middle_block' in layer}							)
							SCREAMING_SNAKE_CASE_	:							List[Any]											={
							    layer_id: [key for key in checkpoint if F"""middle_block.{layer_id}""" in key]
							    for layer_id in range(lowerCAmelCase_							)
							}

							# Retrieves the keys for the output blocks only
							SCREAMING_SNAKE_CASE_	:							Optional[Any]											=len({'.'.join(layer.split('.'							)[:2]							) for layer in checkpoint if 'output_blocks' in layer}							)
							SCREAMING_SNAKE_CASE_	:							int											={
							    layer_id: [key for key in checkpoint if F"""output_blocks.{layer_id}""" in key]
							    for layer_id in range(lowerCAmelCase_							)
							}

							for i in range(1       ,lowerCAmelCase_							):
														SCREAMING_SNAKE_CASE_	:							List[Any]											=(i - 1) // (config["""num_res_blocks"""] + 1)
														SCREAMING_SNAKE_CASE_	:							Tuple											=(i - 1) % (config["""num_res_blocks"""] + 1)

														SCREAMING_SNAKE_CASE_	:							Tuple											=[key for key in input_blocks[i] if F"""input_blocks.{i}.0""" in key]
														SCREAMING_SNAKE_CASE_	:							str											=[key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]

														if F"""input_blocks.{i}.0.op.weight""" in checkpoint:
																					SCREAMING_SNAKE_CASE_	:							Any											=checkpoint[
																					    F"""input_blocks.{i}.0.op.weight"""
																					]
																					SCREAMING_SNAKE_CASE_	:							Any											=checkpoint[
																					    F"""input_blocks.{i}.0.op.bias"""
																					]
																					continue

														SCREAMING_SNAKE_CASE_	:							List[Any]											=renew_resnet_paths(lowerCAmelCase_							)
														SCREAMING_SNAKE_CASE_	:							str											={"""old""": F"""input_blocks.{i}.0""", """new""": F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
														SCREAMING_SNAKE_CASE_	:							Any											={"""old""": """resnets.2.op""", """new""": """downsamplers.0.op"""}
														assign_to_checkpoint(
														    lowerCAmelCase_       ,lowerCAmelCase_       ,lowerCAmelCase_       ,additional_replacements=[meta_path, resnet_op]       ,config=lowerCAmelCase_							)

														if len(lowerCAmelCase_							):
																					SCREAMING_SNAKE_CASE_	:							Optional[int]											=renew_attention_paths(lowerCAmelCase_							)
																					SCREAMING_SNAKE_CASE_	:							List[str]											={
																					    """old""": F"""input_blocks.{i}.1""",
																					    """new""": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
																					}
																					SCREAMING_SNAKE_CASE_	:							Any											={
																					    F"""input_blocks.{i}.1.qkv.bias""": {
																					        """key""": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
																					        """query""": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
																					        """value""": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
																					    },
																					    F"""input_blocks.{i}.1.qkv.weight""": {
																					        """key""": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
																					        """query""": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
																					        """value""": F"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
																					    },
																					}
																					assign_to_checkpoint(
																					    lowerCAmelCase_       ,lowerCAmelCase_       ,lowerCAmelCase_       ,additional_replacements=[meta_path]       ,attention_paths_to_split=lowerCAmelCase_       ,config=lowerCAmelCase_       ,)

							SCREAMING_SNAKE_CASE_	:							int											=middle_blocks[0]
							SCREAMING_SNAKE_CASE_	:							int											=middle_blocks[1]
							SCREAMING_SNAKE_CASE_	:							Union[str, Any]											=middle_blocks[2]

							SCREAMING_SNAKE_CASE_	:							Tuple											=renew_resnet_paths(lowerCAmelCase_							)
							assign_to_checkpoint(lowerCAmelCase_       ,lowerCAmelCase_       ,lowerCAmelCase_       ,config=lowerCAmelCase_							)

							SCREAMING_SNAKE_CASE_	:							Any											=renew_resnet_paths(lowerCAmelCase_							)
							assign_to_checkpoint(lowerCAmelCase_       ,lowerCAmelCase_       ,lowerCAmelCase_       ,config=lowerCAmelCase_							)

							SCREAMING_SNAKE_CASE_	:							Tuple											=renew_attention_paths(lowerCAmelCase_							)
							SCREAMING_SNAKE_CASE_	:							List[Any]											={
							    """middle_block.1.qkv.bias""": {
							        """key""": """mid_block.attentions.0.key.bias""",
							        """query""": """mid_block.attentions.0.query.bias""",
							        """value""": """mid_block.attentions.0.value.bias""",
							    },
							    """middle_block.1.qkv.weight""": {
							        """key""": """mid_block.attentions.0.key.weight""",
							        """query""": """mid_block.attentions.0.query.weight""",
							        """value""": """mid_block.attentions.0.value.weight""",
							    },
							}
							assign_to_checkpoint(
							    lowerCAmelCase_       ,lowerCAmelCase_       ,lowerCAmelCase_       ,attention_paths_to_split=lowerCAmelCase_       ,config=lowerCAmelCase_							)

							for i in range(lowerCAmelCase_							):
														SCREAMING_SNAKE_CASE_	:							Optional[Any]											=i // (config["""num_res_blocks"""] + 1)
														SCREAMING_SNAKE_CASE_	:							Dict											=i % (config["""num_res_blocks"""] + 1)
														SCREAMING_SNAKE_CASE_	:							Dict											=[shave_segments(lowerCAmelCase_       ,2							) for name in output_blocks[i]]
														SCREAMING_SNAKE_CASE_	:							Optional[Any]											={}

														for layer in output_block_layers:
																					SCREAMING_SNAKE_CASE_	:							List[Any]											=layer.split('.'							)[0], shave_segments(lowerCAmelCase_       ,1							)
																					if layer_id in output_block_list:
																												output_block_list[layer_id].append(lowerCAmelCase_							)
																					else:
																												SCREAMING_SNAKE_CASE_	:							str											=[layer_name]

														if len(lowerCAmelCase_							) > 1:
																					SCREAMING_SNAKE_CASE_	:							Optional[Any]											=[key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
																					SCREAMING_SNAKE_CASE_	:							Dict											=[key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]

																					SCREAMING_SNAKE_CASE_	:							Tuple											=renew_resnet_paths(lowerCAmelCase_							)
																					SCREAMING_SNAKE_CASE_	:							List[str]											=renew_resnet_paths(lowerCAmelCase_							)

																					SCREAMING_SNAKE_CASE_	:							List[str]											={"""old""": F"""output_blocks.{i}.0""", """new""": F"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
																					assign_to_checkpoint(lowerCAmelCase_       ,lowerCAmelCase_       ,lowerCAmelCase_       ,additional_replacements=[meta_path]       ,config=lowerCAmelCase_							)

																					if ["conv.weight", "conv.bias"] in output_block_list.values():
																												SCREAMING_SNAKE_CASE_	:							Optional[Any]											=list(output_block_list.values()							).index(['conv.weight', 'conv.bias']							)
																												SCREAMING_SNAKE_CASE_	:							Union[str, Any]											=checkpoint[
																												    F"""output_blocks.{i}.{index}.conv.weight"""
																												]
																												SCREAMING_SNAKE_CASE_	:							Union[str, Any]											=checkpoint[
																												    F"""output_blocks.{i}.{index}.conv.bias"""
																												]

																												# Clear attentions as they have been attributed above.
																												if len(lowerCAmelCase_							) == 2:
																																			SCREAMING_SNAKE_CASE_	:							List[str]											=[]

																					if len(lowerCAmelCase_							):
																												SCREAMING_SNAKE_CASE_	:							int											=renew_attention_paths(lowerCAmelCase_							)
																												SCREAMING_SNAKE_CASE_	:							Union[str, Any]											={
																												    """old""": F"""output_blocks.{i}.1""",
																												    """new""": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
																												}
																												SCREAMING_SNAKE_CASE_	:							Union[str, Any]											={
																												    F"""output_blocks.{i}.1.qkv.bias""": {
																												        """key""": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
																												        """query""": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
																												        """value""": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
																												    },
																												    F"""output_blocks.{i}.1.qkv.weight""": {
																												        """key""": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
																												        """query""": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
																												        """value""": F"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
																												    },
																												}
																												assign_to_checkpoint(
																												    lowerCAmelCase_       ,lowerCAmelCase_       ,lowerCAmelCase_       ,additional_replacements=[meta_path]       ,attention_paths_to_split=to_split if any('qkv' in key for key in attentions							) else None       ,config=lowerCAmelCase_       ,)
														else:
																					SCREAMING_SNAKE_CASE_	:							str											=renew_resnet_paths(lowerCAmelCase_       ,n_shave_prefix_segments=1							)
																					for path in resnet_0_paths:
																												SCREAMING_SNAKE_CASE_	:							Optional[int]											=""".""".join(['output_blocks', str(lowerCAmelCase_							), path['old']]							)
																												SCREAMING_SNAKE_CASE_	:							Optional[Any]											=""".""".join(['up_blocks', str(lowerCAmelCase_							), 'resnets', str(lowerCAmelCase_							), path['new']]							)

																												SCREAMING_SNAKE_CASE_	:							int											=checkpoint[old_path]

							return new_checkpoint


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE						       =						argparse.ArgumentParser()

							parser.add_argument(
							    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
							)

							parser.add_argument(
							    '--config_file',
							    default=None,
							    type=str,
							    required=True,
							    help='The config json file corresponding to the architecture.',
							)

							parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

							__SCREAMING_SNAKE_CASE						       =						parser.parse_args()

							__SCREAMING_SNAKE_CASE						       =						torch.load(args.checkpoint_path)

							with open(args.config_file) as f:
														__SCREAMING_SNAKE_CASE						       =						json.loads(f.read())

							__SCREAMING_SNAKE_CASE						       =						convert_ldm_checkpoint(checkpoint, config)

							if "ldm" in config:
														del config["ldm"]

							__SCREAMING_SNAKE_CASE						       =						UNetaDModel(**config)
							model.load_state_dict(converted_checkpoint)

							try:
														__SCREAMING_SNAKE_CASE						       =						DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
														__SCREAMING_SNAKE_CASE						       =						VQModel.from_pretrained('/'.join(args.checkpoint_path.split('/')[:-1]))

														__SCREAMING_SNAKE_CASE						       =						LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
														pipe.save_pretrained(args.dump_path)
							except:  # noqa: E722
														model.save_pretrained(args.dump_path)







import argparse
import gc
import json
import os

import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowerCamelCase_    :			int    =	16
lowerCamelCase_    :			str    =	32







def 							A__       (    lowerCamelCase    )	->						int:
				return int(x / 2**20    )

class 							_UpperCamelCase					:






			'''simple docstring'''

			def __enter__(							self			:							int   ):
							gc.collect()
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
							UpperCamelCase_:				str						=    torch.cuda.memory_allocated()
							return self




			def __exit__(							self			:							List[Any]    ,						*snake_case_			:							Union[str, Any]   ):
							gc.collect()
							torch.cuda.empty_cache()
							UpperCamelCase_:				List[str]						=    torch.cuda.memory_allocated()
							UpperCamelCase_:				int						=    torch.cuda.max_memory_allocated()
							UpperCamelCase_:				Optional[int]						=    bamb(self.end - self.begin   )
							UpperCamelCase_:				Tuple						=    bamb(self.peak - self.begin   )
							# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")







def 							A__       (    lowerCamelCase  ,     lowerCamelCase = 16  ,     lowerCamelCase = "bert-base-cased"  ,     lowerCamelCase = 3_20  ,     lowerCamelCase = 1_60  ,     )	->						Dict:
				UpperCamelCase_:				str						=    AutoTokenizer.from_pretrained(lowerCamelCase    )
				UpperCamelCase_:				List[str]						=    load_dataset(
				    """glue"""  ,     """mrpc"""  ,     split={"""train""": F'''train[:{n_train}]''', """validation""": F'''validation[:{n_val}]'''}    )

				def tokenize_function(lowerCamelCase    ):
								# max_length=None => use the model max length (it's actually the default)
								UpperCamelCase_:				Optional[Any]						=    tokenizer(examples["""sentence1"""]  ,     examples["""sentence2"""]  ,     truncation=lowerCamelCase  ,     max_length=lowerCamelCase    )
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				UpperCamelCase_:				Dict						=    datasets.map(
				    lowerCamelCase  ,     batched=lowerCamelCase  ,     remove_columns=["""idx""", """sentence1""", """sentence2"""]  ,     load_from_cache_file=lowerCamelCase    )

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				UpperCamelCase_:				int						=    tokenized_datasets.rename_column("""label"""  ,     """labels"""    )

				def collate_fn(lowerCamelCase    ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								if accelerator.distributed_type == DistributedType.TPU:
												return tokenizer.pad(lowerCamelCase  ,     padding="""max_length"""  ,     max_length=1_28  ,     return_tensors="""pt"""    )
								return tokenizer.pad(lowerCamelCase  ,     padding="""longest"""  ,     return_tensors="""pt"""    )

				# Instantiate dataloaders.
				UpperCamelCase_:				List[str]						=    DataLoader(
				    tokenized_datasets["""train"""]  ,     shuffle=lowerCamelCase  ,     collate_fn=lowerCamelCase  ,     batch_size=lowerCamelCase    )
				UpperCamelCase_:				Optional[Any]						=    DataLoader(
				    tokenized_datasets["""validation"""]  ,     shuffle=lowerCamelCase  ,     collate_fn=lowerCamelCase  ,     batch_size=lowerCamelCase    )

				return train_dataloader, eval_dataloader







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						Union[str, Any]:
				# Initialize accelerator
				UpperCamelCase_:				Any						=    Accelerator()

				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				UpperCamelCase_:				str						=    config["""lr"""]
				UpperCamelCase_:				Any						=    int(config["""num_epochs"""]    )
				UpperCamelCase_:				Tuple						=    int(config["""seed"""]    )
				UpperCamelCase_:				Dict						=    int(config["""batch_size"""]    )
				UpperCamelCase_:				Dict						=    args.model_name_or_path

				set_seed(lowerCamelCase    )
				UpperCamelCase_, UpperCamelCase_:				int						=    get_dataloaders(lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     args.n_train  ,     args.n_val    )

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				UpperCamelCase_:				Any						=    AutoModelForSequenceClassification.from_pretrained(lowerCamelCase  ,     return_dict=lowerCamelCase    )

				# Instantiate optimizer
				UpperCamelCase_:				Tuple						=    (
				    AdamW
				    if accelerator.state.deepspeed_plugin is None
				    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
				    else DummyOptim
				)
				UpperCamelCase_:				List[Any]						=    optimizer_cls(params=model.parameters()  ,     lr=lowerCamelCase    )

				if accelerator.state.deepspeed_plugin is not None:
								UpperCamelCase_:				Any						=    accelerator.state.deepspeed_plugin.deepspeed_config[
								    """gradient_accumulation_steps"""
								]
				else:
								UpperCamelCase_:				List[Any]						=    1
				UpperCamelCase_:				Optional[int]						=    (len(lowerCamelCase    ) * num_epochs) // gradient_accumulation_steps

				# Instantiate scheduler
				if (
				    accelerator.state.deepspeed_plugin is None
				    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
				):
								UpperCamelCase_:				List[Any]						=    get_linear_schedule_with_warmup(
								    optimizer=lowerCamelCase  ,     num_warmup_steps=0  ,     num_training_steps=lowerCamelCase  ,     )
				else:
								UpperCamelCase_:				Dict						=    DummyScheduler(lowerCamelCase  ,     total_num_steps=lowerCamelCase  ,     warmup_num_steps=0    )

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				UpperCamelCase_, UpperCamelCase_, UpperCamelCase_, UpperCamelCase_, UpperCamelCase_:				str						=    accelerator.prepare(
				    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )

				# We need to keep track of how many total steps we have iterated over
				UpperCamelCase_:				str						=    0
				# We also need to keep track of the stating epoch so files are named properly
				UpperCamelCase_:				Dict						=    0

				# Now we train the model
				UpperCamelCase_:				Dict						=    {}
				for epoch in range(lowerCamelCase  ,     lowerCamelCase    ):
								with TorchTracemalloc() as tracemalloc:
												model.train()
												for step, batch in enumerate(lowerCamelCase    ):
																UpperCamelCase_:				Union[str, Any]						=    model(**lowerCamelCase    )
																UpperCamelCase_:				str						=    outputs.loss
																UpperCamelCase_:				List[str]						=    loss / gradient_accumulation_steps
																accelerator.backward(lowerCamelCase    )
																if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

																overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
								accelerator.print("""Memory before entering the train : {}""".format(bamb(tracemalloc.begin    )    )    )
								accelerator.print("""Memory consumed at the end of the train (end-begin): {}""".format(tracemalloc.used    )    )
								accelerator.print("""Peak Memory consumed during the train (max-begin): {}""".format(tracemalloc.peaked    )    )
								accelerator.print(
								    """Total Peak Memory consumed during the train (max): {}""".format(
								        tracemalloc.peaked + bamb(tracemalloc.begin    )    )    )
								UpperCamelCase_:				Optional[Any]						=    tracemalloc.peaked + bamb(tracemalloc.begin    )
								if args.peak_memory_upper_bound is not None:
												assert (
												    train_total_peak_memory[F'''epoch-{epoch}'''] <= args.peak_memory_upper_bound
												), "Peak memory usage exceeded the upper bound"

				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
								with open(os.path.join(args.output_dir  ,     """peak_memory_utilization.json"""    )  ,     """w"""    ) as f:
												json.dump(lowerCamelCase  ,     lowerCamelCase    )







def 							A__       (    )	->						Optional[Any]:
				UpperCamelCase_:				Any						=    argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""    )
				parser.add_argument(
				    """--model_name_or_path"""  ,     type=lowerCamelCase  ,     default="""bert-base-cased"""  ,     help="""Path to pretrained model or model identifier from huggingface.co/models."""  ,     required=lowerCamelCase  ,     )
				parser.add_argument(
				    """--output_dir"""  ,     type=lowerCamelCase  ,     default="""."""  ,     help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""  ,     )
				parser.add_argument(
				    """--peak_memory_upper_bound"""  ,     type=lowerCamelCase  ,     default=lowerCamelCase  ,     help="""The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value."""  ,     )
				parser.add_argument(
				    """--n_train"""  ,     type=lowerCamelCase  ,     default=3_20  ,     help="""Number of training examples to use."""  ,     )
				parser.add_argument(
				    """--n_val"""  ,     type=lowerCamelCase  ,     default=1_60  ,     help="""Number of validation examples to use."""  ,     )
				parser.add_argument(
				    """--num_epochs"""  ,     type=lowerCamelCase  ,     default=1  ,     help="""Number of train epochs."""  ,     )
				UpperCamelCase_:				Optional[int]						=    parser.parse_args()
				UpperCamelCase_:				Union[str, Any]						=    {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
				training_function(lowerCamelCase  ,     lowerCamelCase    )


if __name__ == "__main__":
							main()






